Gaussian Process Methodology for Multi-Frequency Marine Controlled-Source Electromagnetic Profile Estimation in Isotropic Medium

D035 On the Signal Propagation in Marine CSEM L.O. Løseth* (Statoil ASA formerly Dept. of Physics NTNU) & L. Amundsen (Statoil ASA) SUMMARY In marine CSEM (controlled source electromagnetics) and SBL (SeaBed Logging) one explores the subsurface by emitting low-frequency signals from an electric dipole source close to the seabed. The main goal is to determine and characterise possible thin resistive layers within the conductive surroundings beneath the seabed (Eidesmo et al. 2002). A typical geological model from an exploration case includes a sea-surface interface a seabed interface and a thin resistive layer. The electromagnetic (EM) response from a plane

An extensive marine controlled source electromagnetic (CSEM) data set was collected in the Gulf of Mexico targeting gas hydrate deposits at four geologically distinct areas within water depths that varied from 900 to 3000 m. The Joint Industry Project 2 drilled two of these locations, Green Canyon 955 and Walker Ridge 313, providing ground truth for the CSEM data. A third survey location, Mississippi Canyon 118 (MC118), is a designated hydrate observatory. We collected both traditional ocean bottom electromagnetic receiver data as well as continuous profiling data using a novel towed three-axis electric field receiver. The towed receiver measured data at a fixed offset of 300 m behind the transmitter, while the ocean bottom receivers recorded data over a range of offsets as the transmitter passed by each receiver. We inverted the CSEM data using a newly developed parallel goal-oriented adaptive finite element-modeling algorithm for efficient 2.5D imaging of the CSEM data. 2D inversions from MC118 are consistent with 1D apparent resistivity pseudosections and reveal resistive areas associated with the carbonate/hydrate mound. An extensive conductive region exists below this, associated with conductive brines sourced from a deeper more resistive salt body. Away from the mound the resistivity structure is a fairly uniform and homogeneous. We present new 2D inversions of the CSEM data that consider electrical anisotropy and constrained inversions that leverage structural control using key seismic horizons along the profiles.

수문학, 기상학 및 기후학 등에서 필수적인 자료중의 하나인 지상기온 자료는 최근 보건, 생물, 환경 등의 다양한 분야로까지 활용영역이 확대되고 있어 그 중요성이 커지고 있으나 지상관측을 통한 지상기온자료의 취득은 시공간적인 제약이 크기 때문에 실측된 기온자료는 시공간 해상도가 낮아 높은 해상도가 요구되는 연구 분야에서는 활용성에 큰 제약을 갖게 된다. 이를 극복하기 위한 하나의 대안으로 상대적으로 높은 시공간 해상도를 가지고 있는 위성영상자료에서 얻을 수 있는 지표면온도 자료를 이용하여 지상기온을 추정하는 많은 연구들이 수행되어 왔다. 본 연구는 이러한 연구의 일환으로써 기상청에서 제공하고 있는 AWS(Automatic Weather Station)에서 취득된 2010년 지상 온도 자료(AWS data)를 바탕으로 대표적인 지표면 온도자료인 MODIS Land Surface temperature(LST data:MOD11A1)와 지상기온에 영향을 미칠 수 있는 Land Cover Data, DEM(digital elevation model) 등의 보조 자료와 함께 다양한 지구통계 기법들을 이용하여 남한 지역의 지상기온을 추정하였다. 추정 전 2010년 전체(365일) LST자료와 AWS자료와의 차이에 대한 RMSE(Root Mean Square Error)값의 계절별 피복별 분석결과 계절에 따른 RMSE값의 변동계수는 0.86으로 나타났으나 피복에 따른 변동계수는 0.00746으로 나타나 계절별 차이가 피복별 차이보다 큰 것으로 분석 되었다. 계절별 RMSE 값은 겨울철이 가장 낮은 것으로 나타났으며 AWS자료와 LST자료와 보조자료를 이용한 선형 회귀분석결과에서도 겨울철의 결정 계수가 가장 높은 0.818로 나타났으며, 여름철의 경우에는 0.078로 나타나 계절별 차이가 매우 크게 나타났다. 이러한 결과를 바탕으로 지구통계 기법들의 대표적인 방법론인 크리깅 방법 중 일반적으로 많이 사용되고 있는 정규 크리깅, 일반 크리깅, 공동 크리킹, 회귀 크리깅을 이용하여 지상기온을 추정한 후 모델의 정확도를 판단할 수 있는 교차 검증을 실시한 결과 정규 크리깅과 일반 크리깅에 의한 RMSE 값은 1.71, 공동 크리깅과 회귀 크리깅에 의한 RMSE 값은 각각 1.848, 1.63으로 나타나 회귀 크리깅 방법에 의한 추정의 정확도가 가장 높은 것으로 분석되었다. Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

Evaluation of gas hydrate deposits in an active seep area using marine controlled source electromagnetics: Results from Opouawe Bank, Hikurangi Margin, New Zealand

Seabed Logging (SBL), a tool that uses Marine Controlled Source Electromagnetic (CSEM) method to specifically explorer and map conceivable hydrocarbon-bearing prospects. In spite of the fact that the CSEM technique has been utilized by the scholarly community forever three decades. The focal thought of SBL is the controlling of electromagnetic vitality in thin resistive layers inside conductive dregs. Regardless of whether it has been notable for quite a while that electromagnetic signs can engender from a conductive formation to another by means of resistive areas, for example, air or resistive parts of the lithosphere, the application to hydrocarbon investigation has not been created as of not long ago. This may be because of the vulnerability of getting any critical reaction from thin resistive layers, for example, hydrocarbon stores since electromagnetic vitality is profoundly lessened in conductive silt. In this way, amid the early improvement period of the SBL procedure, a scaled research experiment was performed to approve if a thin resistive layer (e.g. hydrocarbons) covered inside conductive media (e.g. dregs) could be remotely recognized by utilizing electric dipoles as sources and receivers. Information from this analysis were well contrasted with a forward modelling code. However, the analysis cant be applied in the stratified media with challenging sediments, oil and sea water is present. This research have presented the consequences of the main scaled SBL analysis by introducing the major contituents of the SBL surveys with respective forward modelling.

In communications theory, Nakagami distribution (NKD) is used to model scattered signals that reach a receiver from different paths. In order to use NKD to model a given set of data, we will have to estimate its parameters from the given data. Method of L-Moments (MLM) is being compared with Method of Moments (MOM) for estimating the parameters of NKD. In this study, we have derived its first two L-moments in closed form and estimated its parameters using simulated data. This study shows that estimates based on MLM are better than MOM, not only in small samples but also in large samples. For evaluation purpose, we calculated Root Mean Square Error (RMSE) and Bias using Monte Carlo simulations.

In this paper, two methods to retrieve the Land Surface Temperature (LST) from thermal infrared data supplied by band 10 and 11 of the Thermal Infrared Sensor (TIRS) onboard the Landsat 8 is compared. The first is mono window algorithm developed by Qin et al. and the second is split window algorithm by Rozenstein et al. The purpose of this study is to perform the spatial distribution of land surface temperature, as well as to determine more accurate algorithm for retrieving land surface temperature by calculated root mean square error (RMSE). Finally, we present comparison the spatial distribution of land surface temperature by both of algorithm, and more accurate algorithm is split window algorithm refers to the root mean square error (RMSE) is 7.69° C.

Marine controlled-source electromagnetic (CSEM) method has proved to be a powerful tool and found its increasing applications for offshore resource exploration and tectonic studies. However, bathymetric variations and anisotropic structures encountered in practical measurements have posed challenges for reliable interpretation of marine CSEM data collected in increasingly complex geologic settings. In this study, we present a Finite element (FE) based forward algorithm for simulating 3-D marine CSEM responses in geologic settings characterized with rough bathymetry and electrical anisotropy. Unstructured tetrahedral meshes are employed to permit precise descriptions of arbitrary seafloor topography. After validating the accuracy of the algorithm, we investigate the effects of bathymetric variations and complicated electric anisotropy separately and jointly on marine CSEM responses. Numerical results have demonstrated that both the bathymetry and electrical anisotropy can cause significant distortions on marine CSEM responses, but to different extent. The effects of full electrical anisotropy and bathymetry have to be taken into consideration for reliable interpretations of marine CSEM data in practice.

We have found how the effects of the air wave in marine controlled-source electromagnetic (CSEM) methods gradually vanish in the sea for shallow waters, and how at the same time they gradually grow below the seafloor, in an effort to comprehend existing detectability definitions. The transition from sea to land is smooth because the sea becomes a thin conductive layer when the water depth is smaller than the skin depth in the sea. We consider the problem of detecting resistive layers at depth associated with hydrocarbon reservoirs, particularly in shallow-water explorations and, specifically, on how the air wave affects detection. Our analysis is based on an integral representation of the electric field in terms of its sensitivity to changes in the electrical conductivity of a 1D profile. Two-dimensional images of the integrands are obtained by plotting the integrand as a function of depth for different offsets. Results include the expected growth of the inhibiting effect of the sea as the water depths decrease. However, we also find that this happens up to a point and that from then on its effect decreases to zero. Regarding the resistive layer at depth, its importance grows to a constant as the water depth decreases to zero. As a function of offsets, there appear first the direct current effects. The induction zone is next and is dominated by contributions from the underlying formations. The third zone, which corresponds to the air wave, is largely dominated by contributions from the sea. The fourth and last zone is the plane-wave asymptote. All four classical zones identified in marine CSEM are also present in land CSEM.

To evaluate and compare the measurement precision and sensitivity to change of the Health Assessment Questionnaire disability index (HAQ DI), the Short Form 36 physical functioning scale (PF-10), and simulated Patient-Reported Outcomes Measurement Information System (PROMIS) physical function computer adaptive tests (CATs) with 5, 10, and 15 items, using item response theory-based simulation studies. The measurement precision of the various physical function instruments was evaluated by calculating root mean square errors (RMSEs) between true physical function levels (latent physical function score) and estimated physical function levels. Measurement precision was evaluated at 9 levels of physical function, with 5,000 simulated response patterns per level. Sensitivity to change was evaluated by the ability of a simple statistical test to detect simulated change scores of small to moderate magnitude (standardized effect sizes 0.20, 0.35, and 0.50). RMSEs were smaller for the PROMIS physical function 15-item CAT (CAT-15) and CAT-10 than for the HAQ DI and PF-10 across all levels of the latent physical function scale. Only marginal improvement in performance was observed for the CAT-15 compared with the CAT-10, and the CAT-5 performed quite similarly to the HAQ DI and PF-10 across most levels of the latent physical function scale. Substantially improved sensitivity to change was observed for the CAT-10 compared with the HAQ DI and PF-10, particularly in detecting moderate effect sizes. Clearly higher measurement precision was observed for the PROMIS CAT compared with the HAQ DI and PF-10. Higher reliability also translated into lower sample size requirements for detecting changes in clinical status.

After decades of research and development, Marine Controlled Source ElectroMagnetics (MCSEM) has come into the application phase for oil and gas exploration. However, presently 3D modeling of MCSEM is far from practical with simple models and much computing time. Based on a forward modeling study of 3D frequency-domain MCSEM over a complicated target body and its sensitivity analysis, we derive a method which can delineate the edges of the resistive reservoir. We use the second derivative of the magnitude versus offset (MVO) curve to define the resistive reservoir edges. For the air-wave-dominated far field zone, we suggest using the less affected apparent resistivities in order to improve the adaptability of the method.

libEMMI_MGFD: A program of marine controlled-source electromagnetic modelling and inversion using frequency-domain multigrid solver

In order to suppress the airwave noise in marine controlled-source electromagnetic (CSEM) data, we propose a 3D deconvolution (3DD) interferometry method with a synthetic aperture source and obtain the relative anomaly coefficient (RAC) of the EM field reflection responses to show the degree for suppressing the airwave. We analyze the potential of the proposed method for suppressing the airwave, and compare the proposed method with traditional methods in their effectiveness. A method to select synthetic source length is derived and the effect of the water depth on RAC is examined via numerical simulations. The results suggest that 3DD interferometry method with a synthetic source can effectively suppress the airwave and enhance the potential of marine CSEM to hydrocarbon exploration.

SUMMARY Modelling marine controlled-source electromagnetic (CSEM) responses in the fictitious time domain is a novel approach, which facilitates the full exploration of EM diffusive properties in the fictitious wave domain (FWD). Concepts, such as reflections, refractions, diffractions and transmissions, which are used for the analysis of elastic wave propagation can thus be adopted in FWD for interpreting CSEM data. In this paper, we use a high-order finite difference time domain (FDTD) algorithm for modelling marine CSEM responses in both the fictitious time domain and the diffusive frequency domain. A complex frequency shifted perfectly matched layer (CFS–PML) boundary condition is adopted to the FDTD modelling. We demonstrate the performance of the CFS–PML boundary condition and validate the high-order FDTD code in the FWD with the half-space sea water model and in the frequency domain with the 1-D canonical reservoir model. We investigate and analyse the propagation characteristics of electromagnetic fields in the FWD, where we apply wave propagation concepts to interpret marine CSEM data. Similarities between wave and field propagations relevant for marine CSEM data are demonstrated through several 1-D to 3-D numerical examples.

Marine controlled source electromagnetic (CSEM) is an efficient method to explore ocean resources. The amplitudes of marine CSEM signals decay rapidly with the measuring offsets. The signal is easily contaminated by various kinds of noise when the offset is large. These noise include instrument internal noise, dipole vibration noise, seawater motion noise and environmental noise Suppressing noise is the key to improve data quality and interpretation accuracy. Sparse representation based denoising method has been used for denoising for a long time. provides a new way to remove noise. Under the framework of sparse representation, the denoising effect is closely related to the chosen transform matrix. This matrix is called dictionary and its column named atom. In general, the stronger the correlation between signal and dictionary is, the sparser representation will be, and further the better the denoising effect will be. In this article, a new method based on dictionary learning is proposed for marine CSEM denoising. Firstly, the signal segments suffering little from noise are captured to compose the training set. Then the learned dictionary is trained from the training set via K-singular value decomposition (K-SVD) algorithm. Finally, the learned dictionary is used to sparsely represent the contaminated signal and reconstruct the filtered one. The effectiveness of the proposed approach is verified by a synthetic data denoising experiment, in which windowed-Fourier-transform (WFT) and wavelet-transform (WT) denoising methods and three dictionaries (discrete-sine-transform (DST) dictionary, DST-wavelet merged dictionary and the learned dictionary) under a sparse representation framework are tested. The results demonstrate the superiority of the proposed dictionary-learning-based denoising method. Finally, the proposed approach is applied to field data denoising process, coupled with DST and DST-wavelet dictionaries based denoising methods. The outcomes further proves that the propsoed approach is effective and superior for marine CSEM data denoising.